Graph-Based Pyramid Global Context Reasoning With a Saliency- Aware Projection for Covid-19 Lung Infections Segmentation

Huang, Huimin; Cai, Meifeng; Lin, Lanfen; Zheng, Jing; Mao, Xiongwei; Qian, Xiaohan; Peng, Zhangli; Zhou, Jianying; Iwamoto, Yutaro; Han, Xian-Hua; Chen, Yen‐Wei; Tong, Ruofeng

doi:10.1109/icassp39728.2021.9413957

Cited by 16 publications

(12 citation statements)

References 24 publications

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“…Convolutional neural network with the saliency-level input for segmentation and the index prediction of the content-based medical image retrieval is shown in [32]. On the other hand, the tree structure with a graph in global context for Covid-19 lung segmentation is also presented in [33]. Abou [42] applied convolutional neural network to detect and classify normal white blood cells.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

2022

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Early detection of retinopathy plays an important role in the care of people with diabetes. Classification of diabetic retinopathy in fundus images is very challenging because the blood vessels in the retinal images are too small. Morphology of objects with multi-level saliency is the recent choice because of the activation of feature extraction. However, the challenges of the input models are very complex with the blood. The color, lighting or context can become the reasons that create the decline of the primary key for training. This paper proposes a method for classification of diabetic retinopathy using saliency and shape detection of objects based on a deep Bottleneck U-Net (DbU-Net) and support vector machines in retinal blood vessels. The proposed method includes four stages: preprocessing, feature extraction using DbU-Net, saliency prediction and classification based on the support vector machine. To evaluate this method, its results are compared to the results of the other methods by using the same datasets of STARE and DRIVE for testing with evaluation criteria such as sensitivity, specificity, and accuracy. The accuracy of the proposed method is about 97.1% in these datasets. To assess the levels of diabetes, the diagnostician must initially identify the retinal image with diabetes or not. The result of this paper may help the diagnostician to easily do this.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

2022

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“…Instead of modeling a patient population, Saha et al (2021) converted edges detected in chest CT and Xray images to graphs and leveraged these for detecting COVID-19. Huang et al (2021) used GCNs to refine the bottleneck features for the binary segmentation of COVID-19 infections. Finally, Di et al (2021) learn an uncertainty-vertex hypergraph to distinguish between community-acquired pneumonia and COVID-19.…”

Section: Gcns For Covid-19mentioning

confidence: 99%

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

Keicher¹,

Burwinkel²,

Bani-Harouni³

et al. 2021

Preprint

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During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. However, when dealing with a newly emerging disease such as COVID-19, the impact of patient-and disease-specific factors (e.g. body weight or known co-morbidities) on the immediate course of disease is by and large unknown. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients is often determined only by acute indicators such as vital signs (e.g. breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic graph-based approach combining both imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality. Additionally, the network segments chest CT images as an auxiliary task and extracts image features and radiomics for feature fusion with the available metadata. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention allow for increased understanding of the patient relationships within the population graph and provide insight into the network's decision-making process.

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“…Recently, SOD in RGB-T image [11], light field image [12], [13], [14], high-resolution image [15], [16], optical remote sensing image [17], [18], [19] and 360 • omnidirectional image [20], [21] have been gradually researched. SOD can benefit many image and video processing tasks, such as image segmentation [22], [23], tracking [24], [25], [26], retrieval [27], compression [28], cropping [29], [30], retargeting [31], quality assessment [32] and activity prediction [33].…”

Section: Introductionmentioning

confidence: 99%

SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

Liu

Tan

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

168

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Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection.It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-theart models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.https://github.com/liuzywen/SwinNet

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Graph-Based Pyramid Global Context Reasoning With a Saliency- Aware Projection for Covid-19 Lung Infections Segmentation

Cited by 16 publications

References 24 publications

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

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